1. Technical Field
One or more embodiments of the present disclosure relate generally to selecting objects in digital visual media. More specifically, one or more embodiments of the present disclosure relate to systems and methods that select objects in digital visual media utilizing interactive deep learning.
2. Background and Relevant Art
Recent years have seen a rapid proliferation in the use of digital visual media. Indeed, with advancements in digital cameras, smartphones, and other technology, the ability to capture, access, and utilize images and video has steadily increased. For instance, businesses now routinely utilize digital visual media for presentations, advertising, recruiting, merchandising, and other purposes. Similarly, individuals now routinely utilize digital visual media for communication, entertainment, or employment purposes.
With the increased propagation of digital visual media has come an increased need for systems that can quickly and efficiently edit and modify digital visual media. For example, consumers now routinely capture digital images and seek to digitally modify the features or objects represented in the digital images. For instance, users seek to select objects represented in digital images to move, modify, copy, paste, or resize the selected objects.
In response to this user demand, some digital object selection systems have been developed that permit a user to identify and select objects within digital images. Although these systems permit users to identify and select some digital objects in digital images, these systems have numerous problems and shortcomings.
For example, some common digital object selection systems permit a user to trace an area within a digital image and select pixels within the traced area. Although such systems allow a user to select pixels in a digital image, they are often rough, over-inclusive, under-inclusive, and/or time consuming. Indeed, systems that rely upon manual tracing by a user commonly fail to provide sufficient precision to accurately select objects. Moreover, in order to increase accuracy, users often spend an exorbitant amount of time attempting to trace an object in a digital image.
Similarly, some common digital object selection systems are trained to identify pixels corresponding to common object classes. For example, some common digital systems are trained to identify and select pixels corresponding to dogs, cats, or other object classes. Although such systems are capable of identifying and selecting common objects, they are limited by the particular classifications with which they are trained. Because the number and type of object classes in the world is so vast, such common digital object selection systems can severely limit a user's ability to identify, select, and modify objects in digital visual media. Moreover, because such common systems identify pixels corresponding to a particular object type, they often have difficulty distinguishing between multiple objects belonging to the same class.
These and other problems exist with regard to identifying objects in digital visual media.